Homework8.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "AutoCluster.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMHtyV7Q3ac0o6+x/LDW+ux",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ogut77/DataScience/blob/main/Homework8.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#First execute this cell go to Runtime and click on Restart Runtime\n",
        "!pip install --upgrade --no-deps statsmodels\n",
        "!pip install tbats"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K3IwgLmJyK4C",
        "outputId": "b2971d1d-754e-478f-af5d-9fe3a3cb7d11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: statsmodels in /usr/local/lib/python3.7/dist-packages (0.10.2)\n",
            "Collecting statsmodels\n",
            "  Downloading statsmodels-0.13.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (9.8 MB)\n",
            "\u001b[K     |████████████████████████████████| 9.8 MB 14.6 MB/s \n",
            "\u001b[?25hInstalling collected packages: statsmodels\n",
            "  Attempting uninstall: statsmodels\n",
            "    Found existing installation: statsmodels 0.10.2\n",
            "    Uninstalling statsmodels-0.10.2:\n",
            "      Successfully uninstalled statsmodels-0.10.2\n",
            "Successfully installed statsmodels-0.13.2\n",
            "Collecting tbats\n",
            "  Downloading tbats-1.1.0-py3-none-any.whl (43 kB)\n",
            "\u001b[K     |████████████████████████████████| 43 kB 1.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from tbats) (1.0.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from tbats) (1.4.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from tbats) (1.21.6)\n",
            "Collecting pmdarima\n",
            "  Downloading pmdarima-1.8.5-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl (1.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.4 MB 20.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: Cython!=0.29.18,>=0.29 in /usr/local/lib/python3.7/dist-packages (from pmdarima->tbats) (0.29.28)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.7/dist-packages (from pmdarima->tbats) (1.24.3)\n",
            "Requirement already satisfied: setuptools!=50.0.0,>=38.6.0 in /usr/local/lib/python3.7/dist-packages (from pmdarima->tbats) (57.4.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from pmdarima->tbats) (1.1.0)\n",
            "Requirement already satisfied: pandas>=0.19 in /usr/local/lib/python3.7/dist-packages (from pmdarima->tbats) (1.3.5)\n",
            "Requirement already satisfied: statsmodels!=0.12.0,>=0.11 in /usr/local/lib/python3.7/dist-packages (from pmdarima->tbats) (0.13.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.19->pmdarima->tbats) (2022.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.19->pmdarima->tbats) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=0.19->pmdarima->tbats) (1.15.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->tbats) (3.1.0)\n",
            "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.7/dist-packages (from statsmodels!=0.12.0,>=0.11->pmdarima->tbats) (21.3)\n",
            "Requirement already satisfied: patsy>=0.5.2 in /usr/local/lib/python3.7/dist-packages (from statsmodels!=0.12.0,>=0.11->pmdarima->tbats) (0.5.2)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=21.3->statsmodels!=0.12.0,>=0.11->pmdarima->tbats) (3.0.8)\n",
            "Installing collected packages: pmdarima, tbats\n",
            "Successfully installed pmdarima-1.8.5 tbats-1.1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from matplotlib import pyplot\n",
        "ap1=pd.read_csv('https://raw.githubusercontent.com/ogut77/DataScience/main/data/h02.csv')\n",
        "ap1=ap1.set_index('Month')\n",
        "ap1.plot()\n",
        "pyplot.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "FyDGxIvGA9d6",
        "outputId": "6efb752d-0e85-4f87-db1c-3f3d1bdc4de0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "2wVAcd_T9mwU",
        "outputId": "e0aafc6e-2348-44d9-83b7-bd6dbe2c46ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Cost\n",
              "Month             \n",
              "1991 Jul  0.429795\n",
              "1991 Aug  0.400906\n",
              "1991 Sep  0.432159\n",
              "1991 Oct  0.492543\n",
              "1991 Nov  0.502369\n",
              "...            ...\n",
              "2008 Feb  0.761822\n",
              "2008 Mar  0.649435\n",
              "2008 Apr  0.827887\n",
              "2008 May  0.816255\n",
              "2008 Jun  0.762137\n",
              "\n",
              "[204 rows x 1 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f04cd6dc-7141-46a1-a87c-0e0e8554b5c4\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Cost</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Month</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1991 Jul</th>\n",
              "      <td>0.429795</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1991 Aug</th>\n",
              "      <td>0.400906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1991 Sep</th>\n",
              "      <td>0.432159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1991 Oct</th>\n",
              "      <td>0.492543</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1991 Nov</th>\n",
              "      <td>0.502369</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008 Feb</th>\n",
              "      <td>0.761822</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008 Mar</th>\n",
              "      <td>0.649435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008 Apr</th>\n",
              "      <td>0.827887</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008 May</th>\n",
              "      <td>0.816255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008 Jun</th>\n",
              "      <td>0.762137</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>204 rows × 1 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f04cd6dc-7141-46a1-a87c-0e0e8554b5c4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f04cd6dc-7141-46a1-a87c-0e0e8554b5c4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f04cd6dc-7141-46a1-a87c-0e0e8554b5c4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using the above data set \n",
        "1)Test stationary of the \n",
        "dataset.Write hypothesis. What is your conclusion at 5% siginifance level?\n",
        "\n",
        "2)Take difference and seasonal difference and test stationay of the dataset.Write hypothesis. What is your conclusion at 5% siginifance level?\n",
        "\n",
        "3) Plot ACF and PACF of series if series' difference and seasonal difference taken .\n",
        "\n",
        "4) Based on the plot of ACF and PACF, come up with two SARIMA model and compare their Akaike Information Criteri metric.\n",
        "\n",
        "5)Split data into train and test. For the test data, use last 12 months observation.\n",
        "\n",
        "6)Plot test, train and forecast values for the SARIMA model chosen at Q4. Evaluate performance of the model in terms of MAE(mean absulate error), MAPE(mean absulate percentage error) and MSE(Mean squared Error).\n",
        "\n",
        "7) Which model is suitable for Holt Winters' method (Multiplicative or Additive).Briefly explain why? Plot test, train and forecast values for the Holt Winters' method. Evaluate performance of the model in terms of MAE(mean absulate error), MAPE(mean absulate percentage error) and MSE(Mean squared Error).\n",
        "\n",
        "8) Which model is suitable for ETS method (Multiplicative or Additive for seasonal, trend and error companent. Do we have dampened model or not. ).Briefly explain why? Plot test, train and forecast values for the ETS method. Evaluate performance of the model in terms of MAE(mean absulate error), MAPE(mean absulate percentage error) and MSE(Mean squared Error).\n",
        "\n",
        "9) Use TBATS model in train data.  Evaluate performance of the model in test data Plot test, train and forecast values for the TBATS  method. in terms of MAE(mean absulate error), MAPE(mean absulate percentage error) and MSE(Mean squared Error).\n",
        "\n",
        "10) Which model gives the lowes MAPE score?"
      ],
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        "id": "vcLgzahCpPZN"
      }
    }
  ]
}